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| import os | |
| import gradio as gr | |
| from pdfminer.high_level import extract_text | |
| from docx import Document | |
| from groq import Groq | |
| from reportlab.lib.pagesizes import letter | |
| from reportlab.pdfgen import canvas | |
| # Initialize Groq client with your API key | |
| key = os.getenv("GroqCV") | |
| client = Groq(api_key=key) | |
| # Function to read PDF files | |
| def read_pdf(file_path): | |
| text = extract_text(file_path) | |
| return text | |
| # Function to read DOCX files | |
| def read_docx(file_path): | |
| doc = Document(file_path) | |
| text = "\n".join([paragraph.text for paragraph in doc.paragraphs]) | |
| return text | |
| # Function to analyze CV for role fit using Llama 3.3 and Groq API | |
| def analyze_cv_with_llama(cv_text, job_description): | |
| completion = client.chat.completions.create( | |
| model="llama-3.3-70b-versatile", | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": ( | |
| "You are a hiring expert with extensive experience in talent acquisition. " | |
| "Analyze the following CV content against a job description and score over 100 in these areas: " | |
| "Relevance to the job, Work experience, Skills, Educational background, Achievements and Impact, " | |
| "and Format and Presentation. Provide detailed scores and feedback for each area." | |
| ) | |
| }, | |
| {"role": "user", "content": f"Job Description:\n{job_description}\n\nCV:\n{cv_text}"} | |
| ], | |
| temperature=0.7, | |
| max_tokens=2048, | |
| top_p=0.9, | |
| stream=False, | |
| stop=None, | |
| ) | |
| # Collect and return the analysis response | |
| analysis_text = ''.join([chunk.message.content for chunk in completion.choices]) | |
| return analysis_text | |
| # Function to generate a PDF with analysis results | |
| def generate_pdf(content, output_path): | |
| pdf = canvas.Canvas(output_path, pagesize=letter) | |
| pdf.setFont("Helvetica", 10) | |
| width, height = letter | |
| y = height - 40 # Start position | |
| for line in content.split("\n"): | |
| pdf.drawString(40, y, line) | |
| y -= 15 # Move down for the next line | |
| if y < 40: # Create a new page if space is insufficient | |
| pdf.showPage() | |
| pdf.setFont("Helvetica", 10) | |
| y = height - 40 | |
| pdf.save() | |
| # Function to handle file upload and job screening | |
| def process_file(file, job_description): | |
| # Save the uploaded file to a temporary location | |
| file_path = file.name | |
| file_extension = os.path.splitext(file_path)[1].lower() | |
| # Read the file content based on its format | |
| if file_extension == ".pdf": | |
| cv_text = read_pdf(file_path) | |
| elif file_extension == ".docx": | |
| cv_text = read_docx(file_path) | |
| else: | |
| return "Unsupported file format", None | |
| # Analyze the CV against the job description | |
| analysis_result = analyze_cv_with_llama(cv_text, job_description) | |
| # Generate PDF with analysis results | |
| output_pdf_path = "cv_analysis_result.pdf" | |
| generate_pdf(analysis_result, output_pdf_path) | |
| return analysis_result, output_pdf_path | |
| # Gradio Interface | |
| def main(): | |
| with gr.Blocks() as iface: | |
| with gr.Row(): | |
| file_input = gr.File(label="Upload CV/Resume (PDF or DOCX)") | |
| job_description_input = gr.Textbox(label="Enter Job Description", lines=5, placeholder="Type the job description here...") | |
| analysis_output = gr.Markdown(label="CV Screening Analysis") | |
| download_button = gr.File(label="Download Analysis as PDF") | |
| def process_input(file, job_description): | |
| if not file or not job_description.strip(): | |
| return "Please upload a file and provide a job description.", None | |
| return process_file(file, job_description) | |
| submit_button = gr.Button("Analyze CV") | |
| submit_button.click(process_input, [file_input, job_description_input], [analysis_output, download_button]) | |
| iface.launch(share=True) | |
| if __name__ == "__main__": | |
| main() | |